{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# file_path='/Users/gengyunxin/Documents/项目/traffic_model/test/data/normalized_pretrain_data_72.csv'\n",
    "# file_path='/Users/gengyunxin/Documents/项目/traffic_model/test/data/processed_finetune_seattle_96.csv'\n",
    "# file_path='/Users/gengyunxin/Documents/项目/traffic_model/giteee/data/normed_finetune_predict_pems_144.csv'\n",
    "# file_path='/Users/gengyunxin/Documents/项目/traffic_model/giteee/data/finetune_predict_pems_144.csv'\n",
    "# '/Users/gengyunxin/Documents/项目/traffic_model/giteee/data/finetune_predict_pems_294_smoothed.csv'\n",
    "file_path=‘"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(49999, 294)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(file_path).values # (4367, 73)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "174.81127466084675"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.327913921763501e-05"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_data = df.shape[0]\n",
    "indices = list(range(num_data))\n",
    "# np.random.shuffle(indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4999"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratio = 0.1\n",
    "split = int(np.floor(ratio * num_data))\n",
    "split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30003"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_idx, test_idx, train_idx = indices[:split*3], indices[split*3:split*4], indices[split*4:]\n",
    "len(train_idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14997"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(valid_idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4999"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "train, valid, test = [], [], []\n",
    "for index in indices:\n",
    "    if index in train_idx:\n",
    "        train.append(list(df[index]))\n",
    "    if index in valid_idx:\n",
    "        valid.append(list(df[index]))\n",
    "    if index in test_idx:\n",
    "        test.append(list(df[index]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.DataFrame(train)\n",
    "valid_df = pd.DataFrame(valid)\n",
    "test_df = pd.DataFrame(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4999, 294)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "174.81127466084675"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.values.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.01271508175459342"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.values.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "94.00452285573084"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_df.values.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100.13893517606384"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.values.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.327913921763501e-05"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.values.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_df.to_csv('/Users/gengyunxin/Documents/项目/traffic_model/test/data/ff_train_96.csv', index=0, header=0)\n",
    "# valid_df.to_csv('/Users/gengyunxin/Documents/项目/traffic_model/test/data/ff_valid_96.csv', index=0, header=0)\n",
    "# test_df.to_csv('/Users/gengyunxin/Documents/项目/traffic_model/test/data/ff_test_96.csv', index=0, header=0)\n",
    "train_df.to_csv('/Users/gengyunxin/Documents/项目/traffic_model/giteee/data/train300smoothed.csv', index=0, header=0)\n",
    "valid_df.to_csv('/Users/gengyunxin/Documents/项目/traffic_model/giteee//data/valid300smoothed.csv', index=0, header=0)\n",
    "test_df.to_csv('/Users/gengyunxin/Documents/项目/traffic_model/giteee/data/test300smoothed.csv', index=0, header=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "1000/32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jLPrZ5ZjsNAuDwwEAVuRPPrt0IVlTTHzgooX7cLGlmNlSkcWe+p4Few0hRHTMqiavlKoEzgPeMK66Vym1Xyn1C6VU9hSPuUcptVMptdPtds+vtbPQ0jNEoTHSpCQ7lfA5z7XFmawudOAP6gmzVWdr95keynJSJy3XhIWX8E0260ucHG3tw+cPxLopQohzmHHIK6UygD8Bn9Za9wE/BqqBzYR6+t+Z7HFa6we11jVa6xqXyzX/Fs9QU89QZNVEi9kUOQm5rjiTzNRQ771vaO77lWqt2Xmmm5qKiSUfZ9po6I+dhJRMNpZkMRLQHG+VoZRCxLMZhbxSykIo4P9Ta/0IgNa6TWsd0FoHgZ8CWxeumbNzqmOA420ezh8TwBXGoltrijLJtId62vMZHVLfNUiHx8f5FRO/wGQbPXmHPWVBlhSOBxuNTUX2N/XEtiFCiHOayegaBfwcOKK1/u6Y68cO07gDOBj95s3NH3c1YFLw9i2jpw62lGezsdSJM9WCwwj5Pu/4nrw/EORTD+/htZOd077GztOh0Tk1lRNDPis1FOxVroxFH1mzWEqzU8lKs3CgceJCbEKI+DGTs46XAh8EDiil9hrXfQm4Uym1GdDAaeAjC9C+OXl8XzNXrHRRkDm6McZnr1vJZ96yEmBMuWZ8T76xe4jH9jbz2N5mXv3CNePGmZ9t55luHPYUVuY7JtwWrslXJ2k9HkLbBm4pz+bVk51orZP2w0yIRDeT0TXbgcn+Bz8V/ebMX793hIauIe7cWj7u+tBepqHLmZGe/PiQD0+gAnjotdN88aY1U77OrjNdbCnPnnQWa066lRSTYkXBxA+AZHLd2gL+crSdo639rCmafutDIcTiS7oZr6c7QrsWVeVNfcIzw5gk1H9WuSY8gcphTzlnGaJ3cITjbR5qJqnHA6TbUvjjxy7hrksWf4z8Yrp2TT5KwbOH2mLdFCHEFJIu5OuMhbPCqy5OxmI2kWY1TyjXhCdQXb+2kAONvXz4P3bwk7+enPD43fWhevz5k9TjwzaXZc1rDH4iyHfY2VKezXNHWmPdFCHEFJIu5E+6BzCp0dE0U8m0WyYp14QmUG1dlk2/z8+2Y25++lIdw/7guPs9fbCVFJNi85jFwJaqq1e5ONjUF9mgXAgRX5Iu5OvcHkqz07ClnHt7vfBmImM19wxR5LSzsTQrcl3nwDB/OTpajthd380fdjXwoYsrk76nPhNXrAzNfdh+4twbmAshYiMJQ37gnKWasMzUiT35lp7QFoEr8jNw2FJ4x5ZS8jJsPHkgVI7QWvN///sw+Q4bn71+5YK0P9GsL3aSk27lpeOLN5tZCDFzSRXygaDmVMfAjJYScNhTJsx4be4doiTLTorZxGP3Xsr9t61jdaGDhq7QydwXj7Wzp76HT127kgyb9OIBTCbFRVU57Kqf/6qeQojoS6qQP97Wz9BIgA0lzmnve3ZN3uPz0+/1U2SMja9yZZBhS6HIaY+ckP3jrkYKMm28q6Z0YQ4gQZVmp9HS643KGv1CiOhKqpAPj3rZUj71qJewzNTxNfm2vtDwycIxE6gAirJSae/3MRIIUuceYH2xE4s5qf7a5q0g086wP7QVohAiviRVWu0+00NuunXakTUADruFvqGRSO+ze2D8Vn5hxU47Woe2EzzdOUBlEs9inauCzNBSy219MsJGiHiTVCG/p76b88qzZzTFPtNuwR/UDI2ElsrtmiLkw+WbvQ09eEeCSbt08HyEv/20Gt+GhFgq9jX0cLApvtdvSpqQH/D5qesYYHPZ9PV4GF2/ptmY5RrelDt7kp48wKvGomUS8hOF1whqk5AXS8yXHj3A/U8cGned1nrC3JpYSpqQD/ciS7KnXlRsrEur80i3mvnUw3vwjgToCm/KnTY+5AuNkH+9LhTyUq6ZKLwzVruEvFhCgkHNSbeHpu6hcdf/bkcDW7/6PO7++ChfJnzID/j8fPGRA+xr6AGgwGE/9wMMlXnpfPtdmzjU3Mezh9voHhzGbjGRah0/icpht+CwpXCqYwBbiomizJk9/1Jit5jJTrNIuUYsKc29Q3hHgrT1+wgER0eW/XlvEz2DIzz40sQlUWIh4UP+wZfq+N2Oer7//AkA8jNt0zxi1A3rCinItPHEvma6BoYn9OLDwj3Vytz0SVedFKGSjZx4FUtJnXsACM3PCffaewaH2XWmG7vFxK9fPzNhwmUsJHTIa6158kALMFquyZ9FT9tkUtyysZi/HnNzpnNgQj0+7KNXVXPn1jK++vb18290kgqFvPTkxdJR5x7d+vJISx+N3YNsO+YmENR85i0r8Y4EZ7QB0UJL6JD/63E3te2hv+hhf5BUixnHLGei3rS+kOFAkDdPd08YWRP27poyvvb2jeO2ExTjFWTaaO2VkBdLx0mjJw/wyYf3cNcvdrCnvpsMWwp3XVJJqsXMq7WxX9MpYUPeHwjyr08eoSI3jUuX5wKhUs1sdyhaV+wkXIHJnqJcI6ZXmp1Ge78PrzEkVYhkV9fhiYy26/f6OdUxQK3bQ0VuGnaLmQurctguIT932465OdHu4fM3rma5K7RByExPuo6VajVH3qipevJieuEJaI3dgzFuiRCL43ibhy3l2aRaQoM1gjq093N5Tuj/wmXL8zjpHogsixIrCRPyweD4dVHePNOFxay4ZnU+ZcZf6mxOuo61rjg0tl568nMXfg/quyTkRfLr8Phw9/tYU+SgyDnaufT5g5QbHZ5Ll+cB8EptbOvyCRHy9z9xiMu/+eK463af6WZ9iRO7xRz55MyfQ08eYF1xaH/SnHTL/Bq6hIXfg/pOCXmR/I629AOwpiiTkuxUnKmj2RH+v7CqwEFehpVXYlyySYiQz7Cl0NI7hD8QmkU27A+yr7GX842FyMKfnAVz7MmvNUJ+qtE1Ynq56VbSrGbqu2L71VSIuegdGuFoa9+M7lvb7uHN010ArC508OW3ruGXf3NBZPnxipxQ+ddkUlxSncf22o6YrtA67VAUpVQZ8CugEAgCD2qtf6CUygF+D1QCp4F3a60XZFHxgkw7QQ0dnmEKnXYONfcy7A9yvrGRdrUrg3edX8q1a/Ln9PwXVeXy+RtXc/WquT1egFKK8pw06rsGpr+zEDF20u2h0zPMjlOdmE0m6twe/udgK/v++XrMU8yFae/3kp1m5S3f/SsAeRk2co0/EDovdai5b9wCiZctz+Pxfc188ZEDfPyq5ZEO6WKayXhDP/A5rfVupZQD2KWUeg74MPCC1vrrSqkvAF8APr8QjQzXvFp6hyh02tnfGFoQaHN5FhDamPtb79o05+e3mE187KrqebdzqSvLSeNMp4S8iH+f+f3eSI4oBfYUM0MjARq7B6nInbh0Se/gCFd880Xee0F55LqzPwsqctM41to/rkZ/6+Zidtd388ieJt6+pTQmIT9tuUZr3aK13m1c7geOACXAbcBDxt0eAm5foDZOWADrcHMfOenWCWu/i9iqyEmjvmtQNg8Rca+ha5B0q5l/u/M8TEpFVqMNz7s526HmXrwjQX67ox6AG9cV8vV3bBh3n3dsKeV/XV5Fypj9JuwWM19/x0Z2fOlaLqicfp+LhTCrmrxSqhI4D3gDKNBat0DogwCYtNahlLpHKbVTKbXT7Z7bPqDhRcLCk20Ot/Sxrjhz1mPixcIqz03DOxLE7ZHlDUT80loz4AvwgYsquHVTMbdtKo4sXTJVyB9uCdXrh/1BbCkmfvi+87hmdcG4+1y7poAv3LR60sdnpVljllczDnmlVAbwJ+DTWuuZnaEAtNYPaq1rtNY1LpdrLm0kJ82K1Wyipc/LSCDIsdZ+1hZlzum5xMIJD6NskGGUIo55fH6GA8HIvJivvn0Dz3/mSvIybNOGPMDqosxxvfV4N6OWKqUshAL+P7XWjxhXtymliozbi4D2hWli6Cx1fqaNtl4vJ90ehgPByIgYET/CQ8fOyDBKEcfO3iDIbjHjTLOwIj+DWvcUId/cR7UrVKtfn2DZM23Iq9B3jJ8DR7TW3x1z0+PAXcblu4DHot+8UUVOO619Xo4Yn6jSk48/JVmpKCUTokR86zRCPjdj/JDp5fkZ1LZ5JpxT8vkD1LZ7uH5dIZ+9biXvv7Bi0doaDTMZXXMp8EHggFJqr3Hdl4CvA39QSt0N1APvWpAWGgoy7Rxs6qW+cwiliMlZanFudouZwky7hLyIa12ecE9+/LyalYUO+n1+mnqGKM0ezZfDzX34g5r1xU7eurFoUdsaDdOGvNZ6OzDVGYNro9ucqRU57Tx3uI3mniHyMmzYUszTP0gsuvKcNKnJi7gWLtfknjX5MVyGOdTcR33XIA++VMd33rUpMvHpgmWxGR0zX7NblzeGynPS8PmD7GnopjhrZlv8icVXnpPGSyfmNopKiMXQeVZNPmx1YSYmBX/c1cj2Ex0MjQR4bG8zO051sSwvfc7LpsRawpwiDu+terzNE9lcW8Sfspw02vpkyWERv7oGfNhSTKSdtdVnqtVMtSuD5w63kWJWVLnSeXxfM2+e7mZrZeLuJZEwIb9szAba0pOPX+HxxuHekhDxpnNgmNz0ycetry8JrUj77poy3rGllL0NPfQOjXBRdeKGfMKUa4qdqVhTTAz7g+OmDYv4kmes49HR76NEPoxFHOoaGCYnY/LFCLdUZPPEvmY+eFEFqVYzb57u4uKqXG7dVLLIrYyehAl5k0lRmZvG8TaPhEccyzP+83QOyKxXEZ+6BoYnjKwJe+8FZVy10hWZ2PfLv9m6mE1bEAlTrgGoNBYOKpKQj1ujPflQuaa9z8vtP3qFN+piv6GxEBBaA8uVMXnIW8ymSMAni4QK+WXGjLPiLCnXxKtwyIfXr9nf2Mvehh7e8+DrvPPHr3KouTeWzRNL3IDPT1ufj2V5yRXk55Iw5RqAd24pxZZinvJTWMReqtVMutVMpzHhpL0/FPYbS53saejhyf0tke0WhVhspzpCS2FXGftCLwUJ1ZNfUeDgs9etlNUn41yew0aH0ZNv7w+tHPrHj17C2qJM9jX2xLBlYqmri4T8xDXjk1VChbxIDLnp1jEh7yMn3Yo1xcSmMif7G3onbMouxGI55R5AqdHze0uBhLyIurwM22i5ps9HvjF2flNpFv0+f6Q3JcRiq+vwUOxMxW5ZOsuiSMiLqBtbrnH3e8k3dvDaXJYFwL6Gnhi1TCx1de6BJVWqAQl5sQDy0q10DQ7jDwRpG9OTr3JlYLeYIstFC7GYgkFNndtD9RI66QoS8mIB5Gfa0Rqae7x0eEZD3mxSLM/P4Fhbf4xbKJaipp4hBoYDrCp0xLopi0pCXkRdjbFh8ZMHWvAHdSTkAVYWODguIS9iIPwNcqmFfEKNkxeJYVWBA5fDxiO7GwEiNfnwbY/sbqJ3cARnmiVWTRRLyEggyPt/9kZkVNfKgqUV8tKTF1GnlOKy5XmcMDZFHrug3EqjF3W8XXrzYnHUtnvYcaqLnWe6KctJJcO2tPq2EvJiQVy/tgCAD11cERlVA6GePMCxVgl5sTgONY+e6F9VsPT2hl5aH2li0dy0oYhd//st5J61BEWR006mPWXcfzwhFtLBpl5SLWYyU1O4cFnirgs/VxLyYsGcHfAQKuVsLM3iYFMvnR4ffznazvoSJ2uKll4PSyyOw819rCly8PA9F2MxL70lUaRcIxbdhlInR1v7ePuPX+Uf/riff3nycKybJJJUMKg51NzL+hIn1hTTklz3SkJeLLpNpU5GApoznYMA1HcNxrhFIlE1dA1yoHHi8tVaa/bUd9PQPcjAcIC1S/ib4rQhr5T6hVKqXSl1cMx19ymlmpRSe40/Ny9sM0Uy2VCaBUB2moV7rqiiucfLSCAY20aJhPPsoVau+95feff//xpDw+M3jv/Va2e444FX+fe/1AKwomBpzXIdayY9+V8CN05y/fe01puNP09Ft1kimRU77VS50nnfheUsd2UQCGqae4Zi3SyRYH76ch0mpRgaCbC9tiNyvXckwL+9cAKAZw62ArDctbTGxo81bchrrV8CuhahLWKJUErx3Geu5HPXraI8N7RDj5RsxGx4RwLsa+jlPReU4bCn8NzhUJj7A0E+/fBeOgdCq6D2+/y4HLYlPfFuPjX5e5VS+41yTvZUd1JK3aOU2qmU2ul2u+fxciKZmE0Kk0lRISEv5uBAUy/DgSAXV+Vy1ap8nj/SjnckwOP7mnn6UCv/+61ruOO8EgCWL7EFyc4215D/MVANbAZagO9MdUet9YNa6xqtdY3L5Zrjy4lkVeCwY00xUd8pIS9mbsepUHGhpjKHD1xYTtfAMD97uY6/HG3H5bBx92XLWFccOtm6PH9ph/ycxslrrdvCl5VSPwX+O2otEkuKyaQoy06NjLQRYib2NvRQ5UonJ93KhVW53LCugAe2ncRsUtywrhClVGQv4aV80hXm2JNXShWN+fUO4OBU9xViOqsLM9nT0I3Wsi2gmJmGrkGq8kY3//inW9ZiUop+r58rV4YqBjWV2XzsqmpuWl801dMsCTMZQvk74DVglVKqUSl1N/BNpdQBpdR+4GrgMwvcTpHErl6dT1ufj4NNstSBmJnmniGKs1Ijv5dmp/Evt6+nJCuVK1aEQt5iNvH5G1fjckyceb2UTFuu0VrfOcnVP1+Atogl6upVLpSC54+0saHUGevmiDjX7x2hz+sfF/IAt59Xwu3GyVYxSma8ipjLzbBxfnk2zxxqjXVTRAJo6fUCTAh5MTkJeREX3rqxiKOt/bIEsZhWkzFxriTLPs09BUjIizjxtk3FmE2KR/Y0xropIs6FZ0dLT35mJORFXMjLsHHVSheP7m6SdWzEOTX3DGE2KfId0pOfCQl5ETfef1E57f0+njrQEuumiDjW3OOlMNOO2bT0lg2eCwl5ETeuWpnPsrx0fvHKaRkzL6YUGj4pvfiZkpAXccNkUtx92TL2NfSw7biscyQm197vIz9TQn6mJORFXHl3TRllOal859ljsW6KiFPtfV7yl/gEp9mQkBdxxZpi4kMXVXKwqY/2fm+smyPizIDPz8BwQE66zoKEvIg760tCs16PtMiYeTFee78PQHrysyAhL+JOeD/OIy2ylo0Yr70v9O0uP1NCfqYk5EXccaZZKHbaJeTFBKM9eSnXzJSEvIhLa4oyOdwsIS/Gk3LN7EnIi7i0piiTuo4BvCOBWDdFxJH2fi9Ws4msJbxn62xJyIu4tLLQQSCoOd05EOumiDji7vPhcthQSma7zpSEvIhL1a7Qrj8n2yXkxaj2ft+S3wRktiTkRVyqygvty3nS7YlxS0Q8aekdolBmu86KhLyIS6lWMyVZqdRJyAuDPxCkvmuQyjF7u4rpSciLuFXlSuekW8o1IqSxe4iRgKbKJSE/GxLyIm5VuzI46fbIipQCgLqO0Le6agn5WZGQF3Gr2pXO4HCAtj5frJsi4kCd8a0ufL5GzMy0Ia+U+oVSql0pdXDMdTlKqeeUUieMn9kL20yxFJVmpwHQ3DsU45aIeHDSPUB2moXsdGusm5JQZtKT/yVw41nXfQF4QWu9AnjB+F2IqCowRlG09spqlALq3B6qXNKLn61pQ15r/RLQddbVtwEPGZcfAm6PbrOEgCJnKORbJOQFcLpzgGUysmbW5lqTL9BatwAYP/OnuqNS6h6l1E6l1E63W3b7ETOXlWbBlmKiVco1S553JHRupjwnLdZNSTgLfuJVa/2g1rpGa13jcrkW+uVEElFKUeS0S09e0NQT+qAvy0mNcUsSz1xDvk0pVQRg/GyPXpOEGFXotNPWJyG/1DV0DQKjJ+PFzM015B8H7jIu3wU8Fp3mCDFekTNVevKCxm6jJy8hP2szGUL5O+A1YJVSqlEpdTfwdeA6pdQJ4DrjdyGiLtyTDwZlQtRS1tA9iNVsknXk5yBlujtore+c4qZro9wWISYoctoZCWg6BnyyG9AS1tg1REl2KiaTLDE8WzLjVcS1VQUOAO5/4jD+QDDGrRGx0tg9SGm2nHSdCwl5EdcurMrlH29cxZP7W3j6UGusmyNipLF7SEJ+jiTkRdz7yBXVFDnt/GlXI71DIxMWLPv1a6d54UhbjFonFtqwP0jnwDCFmRLycyEhL+Ke2aS447wSth13s+n+Z7nh+y9xxtgW0OcP8K9PHeFnL5+KcSvFQunwGJt3Z8pJ17mQkBcJ4T0XlFGSlcqdW8s56R7gv3Y2ArD7TA/ekSC1srlI0nL3h0LelSEhPxfTjq4RIh5U5Kaz/fPXALCnvpuDzb0AvFLbAYSCoHdoBGeqJWZtFAsjEvIyfHJOpCcvEs66YicHm3rRWrO9tgOLOTSsrrZdevPJyO2RkJ8PCXmRcNaXZNLhGaa23cP+xh5uWl8EyKbfySrck8/NkHXk50JCXiSc9SVOAH6+/RRBDe/dWobVbOKk9OSTkrvfZ6xIao51UxKShLxIOGuKMkkxKR7Z3USa1UxNRQ7V+Rn8aXcjTx9sWdS2aK356lNHONjUu6ivu5S4+32ynME8SMiLhJNhS+HjV1UzHAhy4bIcrCkmvvb2DWSnWfnyowcJLOI6N26PjwdfquPXr51ZtNdcatwen9Tj50FCXiSkv7t2Be88v5QPXVwJwOayLP7u2hV0Dgyzp7570doRXgL3zdNnb54moqW93yvDJ+dBQl4kJIvZxLfftYmrV49uSnbVKhcWs+K5w4s3+7XeCPm6joHICUIRPVpr3P3Sk58PCXmRNDLtFi6uzuPhNxt41Rg/v9DqO0e3Jtwpvfmoa+gawjsSpFL2dp0zCXmRVP7PrevId9i4+6GdNHYPLvjrNXQPkpdhxZZiYteZxSsTLRV7GkJ/p5vLsmLbkAQmIS+SSmVeOv/xNxcA8Pf/tY8fPH+C9z74WmT9k2ir7xqkKi+DVYUOjrT2LchrLGX7GnqxW0yRJafF7EnIi6RTmp3GV962lp2nu/ne88fZcaqLz/x+L7Xt/VF/rYauQcpy0lhTmMmRlv4JK2SK+dnb0M2GEicpZomquZK/OZGU7txazutfupanPnk599+2npdPdHDD91/mQGP0xrP7/AFa+7yU56SxpshB18Aw7XLyNWra+70cbO6TUs08SciLpJWXYWNtcSYfvKiCFz53JWal+PPepqg9f0uPF62hJDuVNUWZABxukZJNNHhHArz/p2+QYlLccV5prJuT0CTkxZJQ7crgipV5/M+BlqhtCt7a5wWgMNPOaiPkj0jIR8WrJzs40e7h2+/axNrizFg3J6FJyIsl46b1RTT3evn2s8eiMiu2LRzyTjvOVAslWakcaYl+3X8p2nbMTarFzLVr8qe/szgnCXmxZNyyqYjbNhfzwLaTPLpn/mWb1t7RkAdYU+SQnnwUaK3ZdszNJdW5sihZFMwr5JVSp5VSB5RSe5VSO6PVKCEWgi3FzPffs5kip53nozArtqXXS4YthQxbaO+dNUWZ1Lk9eEcC837upexoaz/1XYNcucoV66YkhWj05K/WWm/WWtdE4bmEWFBKKa5enc/LJ9z4/PML47Y+LwVj9h1dU5RJUMPxNinZzMcPnj9Bhi2FWzYWx7opSUHKNWLJuXZ1PgPDAXacmt8yBK193kipBoiMsJGSzdzVtnt4+lArd1+2jJx02SQkGuYb8hp4Vim1Syl1z2R3UErdo5TaqZTa6Xa75/lyQszfJdV5WM0mtp+Y3/o2bb1eCjNTI79X5KSRZjXLydd5CK/L/7ZNRTFuSfKYb8hfqrXeAtwEfEIpdcXZd9BaP6i1rtFa17hcUmMTsZdqNbOlIovttR08c6g10vN+tbaD/Y09M3qOQFDT1u+j0DlarjGZFBtKnPP+hrCU1XUMYFJQlpMW66YkjXmFvNa62fjZDjwKbI1Go4RYaJctz+NQcx8f+fUubv63l/nDmw186vd7+dKjB2b0+E6Pj0BQU5hpH3f9FStdHG7pk2WH5+h0xwDFWakyqiaK5hzySql0pZQjfBm4HjgYrYYJsZAuXZ4HQHlOGqsLM/nmM8dw9/s42NRHuzH+/VzCE6EKzg75FaFvq9trpTQ5F6c7B1gmywpH1Xx68gXAdqXUPmAH8KTW+unoNEuIhbWxNIt3nl/Kd9+9iVs2Fo1bpXLbsekD+uwx8mHrijPJSbfy0vHFWc8+mWitOdUxQGWuhHw0pcz1gVrrOmBTFNsixKIxmxTfflfon6/DbuFbzxyjJCuVoNY8f6SNd19QFrnvqyc7+NIjB0i3pfDruy8kJ906brbrWCaT4rLlebx8ooNgUGMyqcU7qATXNTBMv9cvPfkokyGUYslbWZDBqgIHb1mTz/VrC9h23E2/dyRy+7OH2mju8XKkpY8fb6sFQhOhUkyKvPSJ29JdsdJFh8cn68vPUl3HAICEfJRJyIslTynFY/deyj/dspZbN5cw7A/yzKHRGbHH2/pZU5zJHeeV8tBrZ2jsHqS1z0u+wzZpT/2KFaF6v5RsZi4Q1HzrmWOkW81sKHXGujlJRUJeCMBuMZNiNrGlPIuynFQe2FbLm8aercda+1ld4OCz16/ErBRfeexQaLbrWaWasPxMO6sLHbx8Qk6+ztRzh1vZcaqL+25dR16GbNodTRLyQoyhlOK+t61jwOfn/T97g5dPuOkcGGZloYOSrFQ+d/1K/nK0nVdqOycMnxzripUudp7uZnDYv4itT1z1XaH9eG9cXxjjliQfCXkhznLtmgKe/OTlZKVa+ODPdwCwujC0x+idW8uxpoT+25x90nWsK1a4GA4EeaNOJkbNRIdnGLvFFFnsTUSPhLwQk8jLsPHA+7dEfl9lhHy6LSWyHd25evI1ldnYLSYe2dPEKeOEopiau9+Hy2FDKRmNFG0S8kJMoaYyh198uIYPX1I5rk58fkU2AOfad8RuMXNJdR5P7GvmjgdekeWHp+Hu90ktfoFIyAtxDtesLuC+W9eNu+5jV1Xznpoy3lVz7r1Hv/72DXzp5tX0DI6w7Vj7QjYz4XV4fLgk5BeEhLwQs5Rpt/CNd26ctueZn2nn7suqcDlsPLI7ehuIJ6NwuUZEn4S8EAvIbFLcvrmYF4+1z2hNnKXIHwjSNTgs5ZoFIiEvxAJ7/4UV+IOa37xRH+umxA2fP0CnsV5Q18AwWiM9+QUiIS/EAqvMS+eaVfn89o0zDA3LCdh+7wjv/PFrXPmtbeyu76bdWJZZevILQ0JeiEXwkSur6fAM8+vXTwMwEgjyp12NS3LUzVceO8SRlj4c9hTu/uWbnHR7AOnJLxQJeSEWwdZlOVyx0sUD207SOzjCn/c08bn/2scDL9bGummLqr3fyxP7mvnQxZX84sMX0Ds0wjefPgZAvoT8gpDpZUIsks/fuIpbf/gKX3/6CMfbQr3Xn7xUxzvOL6UiSddQDwY1b/vhdswmRc/gCN2Dw/iDmg9cVE6VK4N315Tx8JsNrC/JPOcMYjF30pMXYpGsK3byt5dW8rsdDew6083dly3DlmLis3/Yhz8QjPrruft9eHyLt3ZOe7+Xd//kNe57/BA9g8MAvHm6i0PNfTR1D5GTbqUsO423bSqmypUBwH23ruOpT17OE/dehsUscbQQpCcvxCL6hxtWU5yVyu76Hj55zQo2ljr51MN7+eYzx/jSzWui+lq3/nA77f0+9n7lOhx2S1SfezKP7Wlmx+ku9jR0c6Slj2+8YyN/3NVIqsXMS/94NemTrEtjt5hZW5y54G1bypTW55ibHWU1NTV6586di/Z6QiSCrzx2kF+9doZ8hw2bxcQHLqzgI1dWz+s5PT4/6//5GQAuX5HHTz9Ug92yMJtjN/cM8fCbDTx/uA2TCe6+bBmf+f2+yO23birm3+48b0Fee6lQSu3SWtfM5bHSkxcixr5yy1oKMu00dA1ypnOQr/3PUTo8Pr5w0xrMc9w+8JixK9WN6wp55nArH/6PHfzjjatJt6ZEFlsbS2vNtmNu3jjVRW27h9s2F/O2TcWTPveh5l7ePNXFBy6q4FTHAJ/47e7IOYZ/uGEVd5wXOsdwumMAj8/PdWsL5nQMIjqkJy9EHAkGNfc9cYhfvXaGrcty+OodGyjLSWUkoGe1DO+vXz/DP/35INs/fzU7T3fzj3/cz7BR97/r4go+fOkyGrsHubgqlxSzif984wxffvQgFrMy9rD18Z6aMv751rWkWce/7jt+/Cq7znTjsKXQ7/NjMSs+ec0Kttd28P33bqbImRrVvxMhPXkhkobJpPg/t61nQ4mT//vfh7npBy/hsFsY8Pl534XlfOyqavIdo6NQeodG+N5zx1ld6OCVk52sK87kzq3lHGnpI9OeQklWKqXnpbGq0MHxtn721Pfwy1dP89BrZwBYnp/BbZuK+dG2Wi5fkcfP7qrBpBTff/44D2w7yQtH2/n4VdW878JymnqGONDYy64z3Vy3toB0q5kLluVwzep8ipyp/N21K2L11ybOYV49eaXUjcAPADPwM6311891f+nJCzFzHR4f337mGN2Dw2TYLPx5bxMKuHKli1s2FXFpdR4PbDvJL189DUCGLQWPz0+a1czgcICtlTn84aMXT3jeHae62HGqk+KsVH70Yi0n3QNsKsvipx88n/wxa+TvOtPNt585xmt1nThTLXh8fgJBjdVs4vUvXUtOunWR/ibEfHrycw55pZQZOA5cBzQCbwJ3aq0PT/UYCXkh5u50xwC/21HP4/uaaekdXezszq1l3HFeKeuKMzndOcB/vHKaJ/e3cO81y/nE1cvP+Zz+QJDTnQNUuzKm3LDj9bpOfv9mA85UC2uLM0mzmrll4+T1erEwYhXyFwP3aa1vMH7/IoDW+mtTPUZCXoj5CwY1B5p62XGqi1OdA/zjDavIShvfq9Zayy5LSSRWNfkSoGHM743AhWffSSl1D3APQHl5+TxeTggBobr9prIsNhnbEE5GAl6EzWeK2WT/iiZ8LdBaP6i1rtFa17hcrnm8nBBCiNmaT8g3AmVjfi8FmufXHCGEENE0n5B/E1ihlFqmlLIC7wUej06zhBBCRMOca/Jaa79S6l7gGUJDKH+htT4UtZYJIYSYt3lNhtJaPwU8FaW2CCGEiDJZ21MIIZKYhLwQQiQxCXkhhEhii7oKpVLKDZyZ48PzgI4oNiceyDElBjmmxJDMx1ShtZ7TRKNFDfn5UErtnOu03nglx5QY5JgSgxzT5KRcI4QQSUxCXgghklgihfyDsW7AApBjSgxyTIlBjmkSCVOTF0IIMXuJ1JMXQggxSxLyQgiRxBIi5JVSNyqljimlapVSX4h1e+ZKKXVaKXVAKbVXKbXTuC5HKfWcUuqE8TM71u08F6XUL5RS7Uqpg2Oum/IYlFJfNN63Y0qpG2LT6nOb4pjuU0o1Ge/VXqXUzWNui+tjUkqVKaVeVEodUUodUkp9yrg+Yd+ncxxTIr9PdqXUDqXUPuOY7jeuj+77pLWO6z+EVrg8CVQBVmAfsDbW7ZrjsZwG8s667pvAF4zLXwC+Eet2TnMMVwBbgIPTHQOw1ni/bMAy4300x/oYZnhM9wF/P8l94/6YgCJgi3HZQWgv5rWJ/D6d45gS+X1SQIZx2QK8AVwU7fcpEXryW4FarXWd1noYeBi4LcZtiqbbgIeMyw8Bt8euKdPTWr8EdJ119VTHcBvwsNbap7U+BdQSej/jyhTHNJW4PyatdYvWerdxuR84Qmi7zoR9n85xTFNJhGPSWmuP8avF+KOJ8vuUCCE/2V6y53pz45kGnlVK7TL2vgUo0Fq3QOgfMpAfs9bN3VTHkOjv3b1Kqf1GOSf8lTmhjkkpVQmcR6iXmBTv01nHBAn8PimlzEqpvUA78JzWOurvUyKE/Iz2kk0Ql2qttwA3AZ9QSl0R6wYtsER+734MVAObgRbgO8b1CXNMSqkM4E/Ap7XWfee66yTXJcoxJfT7pLUOaK03E9o+datSav057j6nY0qEkE+avWS11s3Gz3bgUUJftdqUUkUAxs/22LVwzqY6hoR977TWbcZ/wCDwU0a/FifEMSmlLITC8D+11o8YVyf0+zTZMSX6+xSmte4BtgE3EuX3KRFCPin2klVKpSulHOHLwPXAQULHcpdxt7uAx2LTwnmZ6hgeB96rlLIppZYBK4AdMWjfrIX/kxnuIPReQQIck1JKAT8HjmitvzvmpoR9n6Y6pgR/n1xKqSzjcirwFuAo0X6fYn2GeYZnoW8mdDb9JPDlWLdnjsdQRejM+D7gUPg4gFzgBeCE8TMn1m2d5jh+R+hr8QihnsXd5zoG4MvG+3YMuCnW7Z/FMf0aOADsN/5zFSXKMQGXEfoavx/Ya/y5OZHfp3McUyK/TxuBPUbbDwJfMa6P6vskyxoIIUQSS4RyjRBCiDmSkBdCiCQmIS+EEElMQl4IIZKYhLwQQiQxCXkhhEhiEvJCCJHE/h/XhBxXoPk1KQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_df.shape\n",
    "test_df.iloc[0,:288].plot() # 294"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_df.iloc[2,:288].plot() # 289"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_df.iloc[1].plot() # 300"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.read_csv('/Users/gengyunxin/Documents/项目/traffic_model/giteee/data/test300smoothed.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>61.31542998857875</th>\n",
       "      <th>60.262858323815635</th>\n",
       "      <th>59.55238193651302</th>\n",
       "      <th>60.126984947094186</th>\n",
       "      <th>59.272487455911815</th>\n",
       "      <th>59.06040621325985</th>\n",
       "      <th>58.38367184438321</th>\n",
       "      <th>56.653059870319346</th>\n",
       "      <th>55.210883225266116</th>\n",
       "      <th>56.00906935438843</th>\n",
       "      <th>...</th>\n",
       "      <th>67.31409529689007</th>\n",
       "      <th>64.92841274740839</th>\n",
       "      <th>64.94034395617366</th>\n",
       "      <th>64.11695329681139</th>\n",
       "      <th>60.9307944140095</th>\n",
       "      <th>60.44232867834125</th>\n",
       "      <th>60.36860723195104</th>\n",
       "      <th>57.80717269329253</th>\n",
       "      <th>57.50597724441046</th>\n",
       "      <th>55.92164770367538</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4998.000000</td>\n",
       "      <td>4998.000000</td>\n",
       "      <td>4998.000000</td>\n",
       "      <td>4998.000000</td>\n",
       "      <td>4998.000000</td>\n",
       "      <td>4998.000000</td>\n",
       "      <td>4998.000000</td>\n",
       "      <td>4998.000000</td>\n",
       "      <td>4998.000000</td>\n",
       "      <td>4998.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4998.000000</td>\n",
       "      <td>4998.000000</td>\n",
       "      <td>4998.000000</td>\n",
       "      <td>4998.000000</td>\n",
       "      <td>4998.000000</td>\n",
       "      <td>4998.000000</td>\n",
       "      <td>4998.000000</td>\n",
       "      <td>4998.000000</td>\n",
       "      <td>4998.000000</td>\n",
       "      <td>4998.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.454721</td>\n",
       "      <td>54.440535</td>\n",
       "      <td>54.562414</td>\n",
       "      <td>54.659888</td>\n",
       "      <td>54.666102</td>\n",
       "      <td>54.634711</td>\n",
       "      <td>54.554643</td>\n",
       "      <td>54.553143</td>\n",
       "      <td>54.634570</td>\n",
       "      <td>54.758877</td>\n",
       "      <td>...</td>\n",
       "      <td>54.540657</td>\n",
       "      <td>54.638756</td>\n",
       "      <td>54.642141</td>\n",
       "      <td>54.609781</td>\n",
       "      <td>54.527325</td>\n",
       "      <td>54.527673</td>\n",
       "      <td>54.609128</td>\n",
       "      <td>54.737332</td>\n",
       "      <td>54.759168</td>\n",
       "      <td>54.645912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>32.089802</td>\n",
       "      <td>32.127740</td>\n",
       "      <td>32.270705</td>\n",
       "      <td>32.359420</td>\n",
       "      <td>32.329106</td>\n",
       "      <td>32.285818</td>\n",
       "      <td>32.188586</td>\n",
       "      <td>32.159419</td>\n",
       "      <td>32.187253</td>\n",
       "      <td>32.278698</td>\n",
       "      <td>...</td>\n",
       "      <td>32.280292</td>\n",
       "      <td>32.370289</td>\n",
       "      <td>32.339287</td>\n",
       "      <td>32.294627</td>\n",
       "      <td>32.195682</td>\n",
       "      <td>32.168994</td>\n",
       "      <td>32.196082</td>\n",
       "      <td>32.290602</td>\n",
       "      <td>32.312988</td>\n",
       "      <td>32.221605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>9.125985</td>\n",
       "      <td>9.271654</td>\n",
       "      <td>9.226378</td>\n",
       "      <td>9.355315</td>\n",
       "      <td>9.296096</td>\n",
       "      <td>9.246747</td>\n",
       "      <td>9.205622</td>\n",
       "      <td>9.171352</td>\n",
       "      <td>9.142793</td>\n",
       "      <td>9.118994</td>\n",
       "      <td>...</td>\n",
       "      <td>9.226378</td>\n",
       "      <td>9.355315</td>\n",
       "      <td>9.296096</td>\n",
       "      <td>9.246747</td>\n",
       "      <td>9.205622</td>\n",
       "      <td>9.171352</td>\n",
       "      <td>9.142793</td>\n",
       "      <td>9.118994</td>\n",
       "      <td>9.181418</td>\n",
       "      <td>9.151182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>26.172867</td>\n",
       "      <td>26.097737</td>\n",
       "      <td>26.228945</td>\n",
       "      <td>26.303381</td>\n",
       "      <td>26.400897</td>\n",
       "      <td>26.384113</td>\n",
       "      <td>26.291536</td>\n",
       "      <td>26.195931</td>\n",
       "      <td>26.052096</td>\n",
       "      <td>25.900219</td>\n",
       "      <td>...</td>\n",
       "      <td>26.171188</td>\n",
       "      <td>26.234225</td>\n",
       "      <td>26.339510</td>\n",
       "      <td>26.324613</td>\n",
       "      <td>26.203709</td>\n",
       "      <td>26.145932</td>\n",
       "      <td>26.022937</td>\n",
       "      <td>25.848743</td>\n",
       "      <td>25.848861</td>\n",
       "      <td>25.997429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>47.394001</td>\n",
       "      <td>47.345494</td>\n",
       "      <td>47.416959</td>\n",
       "      <td>47.479390</td>\n",
       "      <td>47.504411</td>\n",
       "      <td>47.480848</td>\n",
       "      <td>47.517509</td>\n",
       "      <td>47.475654</td>\n",
       "      <td>47.527114</td>\n",
       "      <td>47.519568</td>\n",
       "      <td>...</td>\n",
       "      <td>47.416042</td>\n",
       "      <td>47.464713</td>\n",
       "      <td>47.494915</td>\n",
       "      <td>47.477324</td>\n",
       "      <td>47.505709</td>\n",
       "      <td>47.464868</td>\n",
       "      <td>47.522542</td>\n",
       "      <td>47.514633</td>\n",
       "      <td>47.534608</td>\n",
       "      <td>47.498020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>85.149525</td>\n",
       "      <td>85.095057</td>\n",
       "      <td>85.392718</td>\n",
       "      <td>85.444853</td>\n",
       "      <td>85.229226</td>\n",
       "      <td>84.984445</td>\n",
       "      <td>84.649662</td>\n",
       "      <td>84.922572</td>\n",
       "      <td>85.174600</td>\n",
       "      <td>85.316185</td>\n",
       "      <td>...</td>\n",
       "      <td>85.392718</td>\n",
       "      <td>85.444853</td>\n",
       "      <td>85.229226</td>\n",
       "      <td>84.984445</td>\n",
       "      <td>84.655502</td>\n",
       "      <td>84.959254</td>\n",
       "      <td>85.194778</td>\n",
       "      <td>85.316185</td>\n",
       "      <td>85.647537</td>\n",
       "      <td>85.555695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>142.500406</td>\n",
       "      <td>143.250339</td>\n",
       "      <td>143.141518</td>\n",
       "      <td>140.713023</td>\n",
       "      <td>143.454418</td>\n",
       "      <td>147.378682</td>\n",
       "      <td>143.648901</td>\n",
       "      <td>143.874085</td>\n",
       "      <td>143.228404</td>\n",
       "      <td>140.857003</td>\n",
       "      <td>...</td>\n",
       "      <td>143.141518</td>\n",
       "      <td>140.713023</td>\n",
       "      <td>143.454418</td>\n",
       "      <td>147.378682</td>\n",
       "      <td>143.648901</td>\n",
       "      <td>143.874085</td>\n",
       "      <td>143.228404</td>\n",
       "      <td>140.857003</td>\n",
       "      <td>142.714169</td>\n",
       "      <td>141.000488</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 300 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       61.31542998857875  60.262858323815635  59.55238193651302  \\\n",
       "count        4998.000000         4998.000000        4998.000000   \n",
       "mean           54.454721           54.440535          54.562414   \n",
       "std            32.089802           32.127740          32.270705   \n",
       "min             9.125985            9.271654           9.226378   \n",
       "25%            26.172867           26.097737          26.228945   \n",
       "50%            47.394001           47.345494          47.416959   \n",
       "75%            85.149525           85.095057          85.392718   \n",
       "max           142.500406          143.250339         143.141518   \n",
       "\n",
       "       60.126984947094186  59.272487455911815  59.06040621325985  \\\n",
       "count         4998.000000         4998.000000        4998.000000   \n",
       "mean            54.659888           54.666102          54.634711   \n",
       "std             32.359420           32.329106          32.285818   \n",
       "min              9.355315            9.296096           9.246747   \n",
       "25%             26.303381           26.400897          26.384113   \n",
       "50%             47.479390           47.504411          47.480848   \n",
       "75%             85.444853           85.229226          84.984445   \n",
       "max            140.713023          143.454418         147.378682   \n",
       "\n",
       "       58.38367184438321  56.653059870319346  55.210883225266116  \\\n",
       "count        4998.000000         4998.000000         4998.000000   \n",
       "mean           54.554643           54.553143           54.634570   \n",
       "std            32.188586           32.159419           32.187253   \n",
       "min             9.205622            9.171352            9.142793   \n",
       "25%            26.291536           26.195931           26.052096   \n",
       "50%            47.517509           47.475654           47.527114   \n",
       "75%            84.649662           84.922572           85.174600   \n",
       "max           143.648901          143.874085          143.228404   \n",
       "\n",
       "       56.00906935438843  ...  67.31409529689007  64.92841274740839  \\\n",
       "count        4998.000000  ...        4998.000000        4998.000000   \n",
       "mean           54.758877  ...          54.540657          54.638756   \n",
       "std            32.278698  ...          32.280292          32.370289   \n",
       "min             9.118994  ...           9.226378           9.355315   \n",
       "25%            25.900219  ...          26.171188          26.234225   \n",
       "50%            47.519568  ...          47.416042          47.464713   \n",
       "75%            85.316185  ...          85.392718          85.444853   \n",
       "max           140.857003  ...         143.141518         140.713023   \n",
       "\n",
       "       64.94034395617366  64.11695329681139  60.9307944140095  \\\n",
       "count        4998.000000        4998.000000       4998.000000   \n",
       "mean           54.642141          54.609781         54.527325   \n",
       "std            32.339287          32.294627         32.195682   \n",
       "min             9.296096           9.246747          9.205622   \n",
       "25%            26.339510          26.324613         26.203709   \n",
       "50%            47.494915          47.477324         47.505709   \n",
       "75%            85.229226          84.984445         84.655502   \n",
       "max           143.454418         147.378682        143.648901   \n",
       "\n",
       "       60.44232867834125  60.36860723195104  57.80717269329253  \\\n",
       "count        4998.000000        4998.000000        4998.000000   \n",
       "mean           54.527673          54.609128          54.737332   \n",
       "std            32.168994          32.196082          32.290602   \n",
       "min             9.171352           9.142793           9.118994   \n",
       "25%            26.145932          26.022937          25.848743   \n",
       "50%            47.464868          47.522542          47.514633   \n",
       "75%            84.959254          85.194778          85.316185   \n",
       "max           143.874085         143.228404         140.857003   \n",
       "\n",
       "       57.50597724441046  55.92164770367538  \n",
       "count        4998.000000        4998.000000  \n",
       "mean           54.759168          54.645912  \n",
       "std            32.312988          32.221605  \n",
       "min             9.181418           9.151182  \n",
       "25%            25.848861          25.997429  \n",
       "50%            47.534608          47.498020  \n",
       "75%            85.647537          85.555695  \n",
       "max           142.714169         141.000488  \n",
       "\n",
       "[8 rows x 300 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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